Analysis of the Effect of Missing Weather Data on Estimating Daily Reference Evapotranspiration Under Different Climatic Conditions

Numerous equations exist for estimating reference evapotranspiration (ETo). Relationships were often subject to rigorous local calibration, hence having limited global validity. The Penman–Monteith (P − M) equation is widely perceived as the best equation for estimating daily and monthly ETo in all climates. The main shortcoming of the P − M equation is that it requires numerous weather data that may not always be available. This study evaluates the methods to estimate missing data in the context of their influence on the performance of the ETo equations. The performance of other ETo equations under missing data are also compared. ETo equations are ranked individually in semi − humid and semi − arid climates based on their accuracy. Results indicate that the P − M equation is more sensitive in semi − arid climate than semi − humid climate under missing data conditions. The accuracy of the P − M equation under these conditions increases remarkably if any available relationships between dew point and minimum temperatures and also long–term average wind speed for each station are exploited. Finally, the minimum data requirements necessary for adequate performance of the P − M equation are air temperature for semi − humid climates, air temperature and wind speed for semi − arid climates, and the availability of a relationship between dew point and minimum temperature, especially for semi − arid climate. In absence of the satisfaction of such minimum requirements, the Hargreaves–Samani equation is preferable for semi − humid climates and the Hargreaves equation modified by Droogers and Allen ( 2002 ) for semi − arid climates. Copyright Springer Science+Business Media Dordrecht 2015